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Isometric Propagation Network for Generalized Zero-shot Learning

ICLR 2021 · Label-efficient and robust learning › Zero-/few-shot and meta-learning · approach: Generative feature synthesis for zero-shot · 11 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
add visual prototypes to the semantic-prototype classifier (ablation)IPN · AWA2unseen-class accuracy: 61.1% to 67.5%Helped
use only visual prototypes instead of semantic prototypes (ablation)IPN · AWA2unseen-class accuracyHurt
apply propagation only in the visual space (ablation)IPN · AWA2unseen-class accuracy: 2~3%Helped
apply propagation only in the semantic space (ablation)IPN · AWA2unseen-class accuracy: 2~3%Helped
remove propagation from both spaces (ablation)IPN · AWA2unseen-class accuracyHurt
add dual propagation in visual and semantic spaces (ablation)IPN · AWA2HHelped
replace separate attention modules and consistency regularization with one shared visual attention module (ablation)IPN · AWA2seen-class overfittingHurt
replace separate attention modules and consistency regularization with one shared semantic attention module (ablation)IPN · AWA2seen-class overfittingHurt
remove consistency loss while keeping separate attention modules (ablation)IPN · AWA2generalized ZSL performanceNo clear effect
vary the propagation steps from the selected value of 2 (ablation)IPN · AWA2generalized ZSL performanceMixed
vary the temperature γ (ablation)IPN · AWA2accuracyNo clear effect

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